Finalist: Real-Time Water Quality Monitoring with Machine Learning

The Real-Time Water Quality Monitoring with Machine Learning tool is designed to minimize the consumption of contaminated water by using artificial intelligence to detect harmful microbes and bacteria.

The Real-Time Water Quality Monitoring with Machine Learning project uses a CIFAR-10-based convolutional neural network trained to detect harmful bacteria, which will be quantized to 8 bits and run on the RZ/A Stream-it! development kit. Input images captured by a USB microscope. The Arm CMSIS-NN software library ported to the RZ/A Software Package to enable the neural network given the available memory.